[2604.08874] A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout

Computer Science > Machine Learning arXiv:2604.08874 (cs) [Submitted on 10 Apr 2026] Title:A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout Authors:Rafael da Silva, Jeff Eicher, Gregory Longo View a PDF of the paper titled A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout, by Rafael da Silva and 2 other authors View PDF Abstract:This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records. Dropout is operationalized as a time-to-event outcome at the enrollment level; weekly risk is modeled in discrete time via penalized, class-balanced logistic regression over person--period rows. Under a late-event temporal holdout, the model attains row-level AUCs of 0.8350 (train) and 0.8405 (test), with aggregate calibration acceptable but sparsely supported in the highest-risk bins. Ablation analyses indicate performance is sensitive to feature set composition, underscoring the role of temporal engagement signals. A scenario-indexed policy layer produces survival contrasts $\Delta S(T)$ under an explicit trigger/schedule contract: positive contrasts are confined to the shock branch ($T_{\rm policy}=18$: 0.0102, 0.0260, 0.0819), while the mechanism-aware branch is negative ($\Delta S_{\rm mech}(18)=-0.0078$, $\Delta S_{\rm mech}(38)=-0.0134$)....